Oliver Grainge

Oliver Grainge

AI Researcher | PhD candidate in Machine Intelligence | Contractor | Efficient AI deployment

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

TAT-VPR: Ternary Adaptive Transformer for Dynamic and Efficient Visual Place Recognition

Published in arXiv preprint, 2025

TAT-VPR fuses ternary weight quantization with a learned activation-sparsity gate, giving visual SLAM systems a 5 × smaller model and up to 40 % fewer operations while retaining state-of-the-art Recall@1.

Recommended citation: Grainge, O., Milford, M., Bodala, I., Ramchurn, S. D., & Ehsan, S. (2025). "TAT-VPR: Ternary Adaptive Transformer for Dynamic and Efficient Visual Place Recognition." arXiv preprint, arXiv:2505.16447.
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TeTRA-VPR: A Ternary Transformer Approach for Compact Visual Place Recognition

Published in arXiv preprint, 2025

This paper introduces TeTRA, a ternary transformer approach that progressively quantizes Vision Transformers to achieve significant reductions in memory consumption and inference latency, while preserving or even enhancing visual place recognition performance on resource-constrained platforms.

Recommended citation: Grainge, O., Milford, M., Bodala, I., Ramchurn, S. D., & Ehsan, S. (2025). "TeTRA-VPR: A Ternary Transformer Approach for Compact Visual Place Recognition." arXiv preprint, arXiv:2503.02511. doi:10.48550/arXiv.2503.02511
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Structured Pruning for Efficient Visual Place Recognition

Published in IEEE Robotics and Automation Letters, 2024

This paper introduces a structured pruning method for VPR that strategically removes redundancies in the embedding space, significantly reducing memory usage and latency with minimal impact on accuracy.

Recommended citation: Grainge, O., Milford, M., Bodala, I., Ramchurn, S. D., & Ehsan, S. (2025). "Structured Pruning for Efficient Visual Place Recognition." IEEE Robotics and Automation Letters, 10(2), 2024-2031. doi:10.1109/LRA.2024.3523222
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Design Space Exploration of Low-Bit Quantized Neural Networks for Visual Place Recognition

Published in IEEE Robotics and Automation Letters, 2024

This paper examines the impact of compact architectural designs and quantization techniques on visual place recognition, balancing recall performance with memory and latency constraints for edge deployment.

Recommended citation: Grainge, O., Milford, M., Bodala, I., Ramchurn, S. D., & Ehsan, S. (2024). "Design Space Exploration of Low-Bit Quantized Neural Networks for Visual Place Recognition." IEEE Robotics and Automation Letters. 9(6), 5070-5077. doi:10.1109/LRA.2024.3386459
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